Big Brother is coming. He knows where you are. He’s tracking your every move. Despite whatever dystopian fiction you’ve read, seen or had nightmares about, he’s actually here to help. And he doesn’t go by some tyrannical, cult of personality namesake as ”Big Brother” but rather the far more innocuous title of “Location Analytics.”

So, what is this marvel of modern science? Let me explain it in a generalized manner (which isn’t necessarily the route taken by all companies involved) so we don’t get bogged down in IT jargon. It all starts when a smartphone comes within range of a wireless Internet hub. At this point, the Wi-Fi router detects the mobile’s 16-digit signature (unique to that phone) and vice versa. No information is exchanged between the two machines; it’s simply a handshake to acknowledge each other’s presence.

Now imagine you install Wi-Fi repeater nodes at, say, 100-foot intervals throughout your lobby floor—a large enough distance between the disparate nodes so their ranges don’t overlap. As a smartphone travels throughout the space, it will shake hands and be greeted by whichever node it happens to be in the presence of at that time. And as smartphones tend not to move on their own but remain in the hands or pocket of an actual person, tracking each unique mobile’s displacement from node to node gives a truthful account of a visitor’s real-time movement through the lobby. Most sophisticated nodes also can detect the relative signal strength from your cellular—that is, its distance from the node—and translate that into an individual’s physical position accurate to within a foot.

Moreover, these smartphone greetings are time sensitive. That is, the smartphone doesn’t just shake hands with a Wi-Fi hub then disconnect; they shake each other’s hands until they are no longer within range of one another. In 99.99% of cases that will mean the person carrying the cellphone walks away. Hence, with neatly installed Wi-Fi repeater nodes, a hotelier can track a visitor’s movement through a property as well as how long the visitor spends at each location.

All this depends, of course, on an individual having a smartphone on his or her person and that this mobile actually is powered on with its Wi-Fi detection enabled. Given that having Wi-Fi detection on is the default mode and that nowadays most people would probably consider themselves naked without their smartphones on them at all times, the absence of a Wi-Fi-enabled mobile on each and every visitor is an increasingly rare scenario.

Possibilities for hotels
In practice, doting on one individual user is a wasteful application of this technology. Instead, the power of location analytics rests with aggregating users in a big-data manner—that is, measuring thousands or millions of unique smartphone movements within a monthly, quarterly or yearly period. And this can yield some very fascinating results for hotels. The more Big Brother knows about your every move, the better he can help you with your operational goals.

First, you can develop aggregate heat maps for a property, displaying the high-traffic areas of your hotel in addition to the flow or pathway a guest is most likely to take on his or her way to the elevator corridor, lobby bar or gym facilities. Such heat maps can also be temporal in nature, showing how long the average customer spends at certain locations. These sorts of visual data productions can then be used to ask a series of probing questions.

For instance, why is your second restaurant continually failing to meet sales targets? Could it be simply that it is out of sight and earshot from the regular flow of consumers? Next, why is one of the most frequent questions asked at the front desk about the location of the lobby floor bathrooms? Like the second restaurant, perhaps they aren’t near the most common guest pathways and a well-placed sign or staff member could remedy this issue. Why is it that most visitors to the spa who leave within five minutes have a 20% chance of approaching the front desk or concierge immediately afterward? And naturally stemming from this inquiry, is there anything a concierge should say to his or her customers to explore this issue?

As yet another example, you notice that by far the most frequently sold items in your gift shop are those situated on the rack closest to the store’s entrance. How would a temporal heat map further elucidate this observation? Perhaps less than 10% of visitors actually reach and browse the back aisles while, in the same vein, the average consumer spends more than 75% of his or her time around that front rack. Would it be too radical to suggest a rearrangement of the floor plan to increase the traffic flow toward the back of the gift shop? These questions and inferences are barely scratching the surface as to the power of big-data location analytics. This system of measurements also can be increasingly handy for building quantitative deductions that support one of your intuitive hunches.

Two caveats
There are a couple things you might pick up from these types of questions. First, even a small property requires a lot of Wi-Fi repeater nodes to obtain accurate location-analytics data. It’s not a paltry cost, but at the same time, the results and quantitative conclusions you can draw from the data are worth every penny in helping you optimize your revenues.

Second, this isn’t a panacea insofar as you can’t truly align specific customers with dollars spent onsite. Again, this is because smartphone handshakes are kept anonymous. The Wi-Fi nodes will interpret your cellular as just another phone and not, for example, “Guest From Room 237’s Mobile.” Location-analytics software is capable of matching smartphones with credit card information, but this would be violating a slew of privacy laws (at least here in North America).

The gift shop example works because it attempts to correlate the sales for the whole store with the entirety of consumers who enter the premises whether they make a purchase or not. Inquiring about, say, why patrons at your main restaurant who stay more than two hours for dinner aren’t significantly more likely to buy desserts or additional drinks compared to those who spend less than two hours presupposes that you know how long each visitor stays (which is legally attainable using location analytics) as well as what visitors buy based upon their smartphone’s unique signature (a big no-no as far as the U.S. Government is concerned).

Conclusion
As a quick exercise to help you understand the power of location analytics, try to think up several other prominent uses for this technology outside the ones cited above. How about the ability to monitor check-in and check-out wait times as well as identify the consistently busy periods at the front desk? Furthermore, as big data is all about finding patterns on an immense scale, location analytics can potentially be utilized to compare aggregated heat maps with other hotels, assisting you, for example, in determining how well your breakfast café is doing relative to your competitive set or if you indeed have a lively lobby.

Think about what questions you’d like to have answered and location analytics might have the answer you need. Plus, it has networking power: The more smartphone users and the more properties that install detection systems, the more valuable this technology becomes. Don’t let yourself become a late adapter.

(Article published by Larry Mogelonsky on Hotel News Now on April 18, 2013)